6 citations · 13 across the 5 of their papers we have counts for
5 papers
WWAggr: A Window Wasserstein-based Aggregation for Ensemble Change Point Detection
Alexander Stepikin, Evgenia Romanenkova, Alexey Zaytsev
Change Point Detection (CPD) aims to identify moments of abrupt distribution shifts in data streams. Real-world high-dimensional CPD remains challenging due to data pattern complex…
Learning Transactions Representations for Information Management in Banks: Mastering Local, Global, and External Knowledge
Alexandra Bazarova, Maria Kovaleva, Ilya Kuleshov +7
In today's world, banks use artificial intelligence to optimize diverse business processes, aiming to improve customer experience. Most of the customer-related tasks can be categor…
Continuous-time convolutions model of event sequences
Vladislav Zhuzhel, Vsevolod Grabar, Galina Boeva +9
Event sequences often emerge in data mining. Modeling these sequences presents two main challenges: methodological and computational. Methodologically, event sequences are non-unif…
Deep learning model solves change point detection for multiple change types
Alexander Stepikin, Evgenia Romanenkova, Alexey Zaytsev
A change points detection aims to catch an abrupt disorder in data distribution. Common approaches assume that there are only two fixed distributions for data: one before and anoth…
InDiD: Instant Disorder Detection via Representation Learning
Evgenia Romanenkova, Alexander Stepikin, Matvey Morozov +1
For sequential data, a change point is a moment of abrupt regime switch in data streams. Such changes appear in different scenarios, including simpler data from sensors and more ch…